Spark Streaming and Kafka Integration

Spark streaming and Kafka Integration are the best combinations to build real-time applications. Spark is an in-memory processing engine on top of the Hadoop ecosystem, and Kafka is a distributed public-subscribe messaging system. Kafka can stream data continuously from a source and Spark can process this stream of data instantly with its in-memory processing primitives. By integrating Kafka and Spark, a lot can be done. We can even build a real-time machine learning application.

Spark streaming and Kafka Integration

Before going with Spark streaming and Kafka Integration, let’s have some basic knowledge about Kafka by going through our previous blog on Kafka.

Note down the port number and the topic name here, you need to pass these as parameters in Spark.
After creating a topic below you will get a message that your topic is created.Created topic “acadgild-topic”

You can also check the topic list using the following command:

./bin/kafka-topics.sh --list --zookeeper localhost:2181

Now for sending messages to this topic, you can use the console producer and send messages continuously. You can use the following commands to start the console producer.

This is how you can perform Spark streaming and Kafka Integration in a simpler way by creating the producers, topics, and brokers from the command line and accessing them from the Kafka create stream method.

We hope this blog helped you in understanding how to build an application having Spark streaming and Kafka Integration.